Miller, Ryan (2021) Does Artificial Intelligence Use Private Language? [Preprint]
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Abstract
Wittgenstein’s Private Language Argument holds that language requires rule-following, rule following requires the possibility of error, error is precluded in pure introspection, and inner mental life is known only by pure introspection, thus language cannot exist entirely within inner mental life. Fodor defends his Language of Thought program against the Private Language Argument with a dilemma: either privacy is so narrow that internal mental life can be known outside of introspection, or so broad that computer language serves as a counter-example. I suggest that the developing field of artificial intelligence (deep learning neural networks) tends to vitiate Fodor’s defense and hence vindicate the Private Language Argument. The first horn of Fodor’s dilemma requires language to encompass genuinely internal mental life, i.e. non-projected intentional states, which are not exhibited in classical machine learning but only by deep learning neural networks (artificial intelligence). Such networks act as black boxes, however, whose state cannot be understood by tracking the changes in their supervenience bases without shared context, and that shared context introduces the possibility of error. The language of artificial intelligence is not private.
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Item Type: | Preprint | ||||||
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Keywords: | private language, artificial intelligence, language of thought, computational theory of mind, Fodor, Wittgenstein | ||||||
Subjects: | Specific Sciences > Cognitive Science > Computation Specific Sciences > Artificial Intelligence Specific Sciences > Cognitive Science > Concepts and Representations Specific Sciences > Artificial Intelligence > Machine Learning |
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Depositing User: | Ryan Miller | ||||||
Date Deposited: | 07 Nov 2022 15:34 | ||||||
Last Modified: | 07 Nov 2022 15:34 | ||||||
Item ID: | 21369 | ||||||
Subjects: | Specific Sciences > Cognitive Science > Computation Specific Sciences > Artificial Intelligence Specific Sciences > Cognitive Science > Concepts and Representations Specific Sciences > Artificial Intelligence > Machine Learning |
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Date: | 2021 | ||||||
URI: | https://philsci-archive.pitt.edu/id/eprint/21369 |
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